Many products and systems in the market today for human being identification are generally based upon the analysis of images which are taken from a subject. The process requires high quality of an image of the subject in order to meet the performance requirements of the task. That also means a need for a high computing power for data processing. Variations from environmental conditions and others always impact on the image quality and subsequence upon the system performance.
In another area of applications, generally, a primary task when operating a vehicle, such as, driving an automobile, flying a plane, or conducting a train, is to monitor vehicular movement to ensure safe passage of the vehicle and its contents. At times, however, a vehicle operator can become distracted. Some common distractions include fatigue, talking on or dialing a phone, interacting with passengers, or reading road signs.
Systems have been proposed, wherein devices periodically or randomly require an operator to manually respond to an awareness indicator by pressing a button. If a response is not received, the device generates an alarm altering the operator of potential danger. Other proposed devices attempt to monitor driver awareness based on heart metrics. For example, the device may measure fluctuations in heart rate, blood pressure, or irregular heart beat patterns. While these attempts, allegedly, increase driver awareness during times of fatigue, they are crude measures that are susceptible to false signals.
Other previous systems have included an imager that takes a detailed image of the vehicle driver, and attempts to recognize facial points on the driver in the image, such as eyes, ears, nose, or mouth. Based upon the location of these facial features, the system can determine whether the driver of the vehicle is attentive or non-attentive. These systems typically require large amounts of processing capability to determine the location of the facial features contained in the image.